Generative AI Mental Health Chatbots as Therapeutic Tools: Systematic Review and Meta-Analysis of Their Role in Reducing Mental Health Issues

Background: In recent years, artificial intelligence (AI) has driven the rapid development of AI mental health chatbots. Most current reviews investigated the effectiveness of rule-based or retrieval-based chatbots. To date, there is no comprehensive review that systematically synthesizes the effect of generative AI (GenAI) chatbot's impact on mental health. Objective: This review aims to (1) narratively synthesize existing GenAI mental health chatbots' technical features, treatment and research designs, and sample characteristics through a systematic review of quantitative studies and (2) quantify the effectiveness and key moderators of these rigorously designed trials on GenAI mental health chatbots through a meta-analysis of only randomized controlled trials (RCTs). Methods: The search strategy includes 11 database searching, backward citation tracking, and a manual ad hoc search to update literature. This thorough literature search, completed in March 2025, returned 5555 records for screening. The systematic review included studies that (1) used generative or hybrid (rule/retrieval-based and generative) AI-based chatbots to deliver interventions and (2) quantitatively measured mental health-related outcomes. The meta-analysis has additional inclusion criteria: (1) studies must be RCTs, (2) must measure negative mental health issues, (3) the comparison group must not have chatbot features, and (4) must provide enough statistics for effect size calculation. We followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) checklist and registered the protocol retrospectively during the revision process (September 18, 2025). In meta-regression, data were synthesized in R software using a random-effects model. Results: The narrative synthesis of 26 studies revealed that (1) GenAI chatbot interventions mostly took place in non-WEIRD countries (non-Western, Educated, Industrialized, Rich, and Democratic) and (2) there is a lack of studies focusing on young children and older adults. The meta-analysis of 14 RCTs showed a statistically significant effect (effect size [ES]=0.30, P=.047, N=6314, 95% CI 0.004, 0.59, 95% prediction interval [PI] -0.85, 1.67), which means that GenAI chatbots are, on average, effective in reducing negative mental health issues, such as depression, anxiety, among others. We found that social-oriented chatbots (ie, those that mainly provide social interactions) are more effective than task-oriented programs (ie, those that assist with specific tasks). Risk of bias in the nonrandomized studies and RCTs was assessed using Cochrane ROBINS-I (Risk Of Bias In Non-randomised Studies - of Interventions) and RoB2 (revised Cochrane risk-of-bias tool for randomized trials), respectively, indicating a moderate amount of risk. One main limitation of this meta-analysis is the small number of studies (n=14) included. Conclusions: By identifying research gaps, we suggest that future researchers investigate user groups such as adolescents and older adults, outcomes other than depression and anxiety, cultural adaptations in non-WEIRD countries, ways to streamline chatbots in usual care practices, and explore applications in diverse settings. More importantly, we cannot ignore GenAI chatbots' risks while acknowledging their promise. This review also emphasized several ethical implications.